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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yuke Guo Xiaosheng Zhang Luyu Zhao National Key Laboratory of Radar Detection and Sensing Xidian University Xi'an China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

We proposed a high isolation design between two broadband dual polarization transceiver antennas in this paper. It employs periodic metal isolation walls with equal spacing and height to strongly suppress the low-frequency band (LB) coupling. Covering the vertical and horizontal surfaces of the metal with flexible graphene high resistive metasurfaces that easily conform to the structure improves 40 dB in isolation between different polarizations at 6–18 GHz. This device has less impact on the radiation pattern of the antenna. This design not only enhances the isolation between different polarizations but also optimizes equipment heat dissipation and improves device performance.

关键词： Performance evaluation Heating systems Couplings Graphene Metals Metasurfaces Transceivers Broadband communication Broadband antennas Antenna radiation patterns

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A Joint Learning Sentiment Analysis Method Incorporating Emoji-Augmentation 8

A Joint Learning Sentiment Analysis Method Incorporating Emo...

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8th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2022

作者： Chen, Jie Luo, Luping Ji, Bojing Zhao, Shu Zhang, Yanping Anhui University The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology China Chizhou University School of Big Data and Artificial Intelligence China

ISBN: (纸本)9781665477352

Social media is the platform for most people to share their opinions, emojis are also widely used to express moods, emotions, and feelings on social media. There have been many researched on emojis and sentiment analysis. However, existing methods mainly face two limitations. First, since deep learning relies on large amounts of labeled data, the training samples of emoji are not enough to achieve the training effect. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this paper, we propose a joint learning sentiment analysis method incorporating emoji-augmentation, and the method has two advantages compared with the existing work. First, We optimize the easy data augmentation method so that the newly generated sentences can also preserve the semantic information of emojis, which relieves the problem of insufficient training data with emojis. Second, it fuses emojis and text features to allow the model to better learn the mutual emotional semantics between text and emojis, jointly training emojis and words to obtain the sentence representations containing more semantic information of both emojis and text. Our experimental results show that the proposed method can significantly improve the performance compared with several baselines on two datasets. © 2022 IEEE.

关键词： Sentiment analysis

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Reduced-search guessing random additive noise decoding of polar codes

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Science China(Information Sciences) 2023年第2期66卷 283-284页

作者： Kefan WANG Yuejun WEI Zhenyuan CHEN Huarui YIN Liping LI Wenyi ZHANG Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education of China Anhui University Huawei Technologies Department of Electronic Engineering and Information Science University of Science and Technology of China

Guessing random additive noise decoding(GRAND) has attracted researchers since its introduction in [1]. For an additive noise channel with hard decision output, the algorithm checks all error patterns, and it schedules its search in descending likelihood order. Ordered reliability bit GRAND(ORBGRAND) [2] is a variant of *** soft channel output is available,

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Balancing property optimization and constraint satisfaction for constrained multi-property molecular optimization

arXiv

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arXiv 2024年

作者： Xia, Xin Zhang, Yajie Zeng, Xiangxiang Zhang, Xingyi Zheng, Chunhou Su, Yansen The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China College of Computer Science and Electronic Engineering Hunan University Changsha410012 China

Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development. Various artificial intelligence technologies have demonstrated high effectiveness and efficiency on molecular optimization tasks. However, few of these technologies focus on balancing property optimization with constraint satisfaction, making it difficult to obtain high-quality molecules that not only possess desirable properties but also meet various constraints. To address this issue, we propose a constrained multi-property molecular optimization framework (CMOMO), which is a flexible and efficient method to simultaneously optimize multiple molecular properties while satisfying several druglike constraints. CMOMO improves multiple properties of molecules with constraints based on dynamic cooperative optimization, which dynamically handles the constraints across various scenarios. Besides, CMOMO evaluates multiple properties within discrete chemical spaces cooperatively with the evolution of molecules within an implicit molecular space to guide the evolutionary search. Experimental results show the superior performance of the proposed CMOMO over five state-of-the-art molecular optimization methods on two benchmark tasks of simultaneously optimizing multiple non-biological activity properties while satisfying two structural constraints. Furthermore, the practical applicability of CMOMO is verified on two practical tasks, where it identified a collection of candidate ligands of β2-adrenoceptor GPCR and candidate inhibitors of glycogen synthase kinase-3β with high properties and under drug-like constraints. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Research on the Application of Voice Acoustics for Speech Intelligence in Folk Songs 3rd

Research on the Application of Voice Acoustics for Speech In...

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3rd International Conference on Advanced intelligent Technologies, ICAIT 2022

作者： Lyu, Shiliang Chen, Shuang Qi, Hanyun Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Gansu Provincial Key Laboratory of Intelligent Processing of National Languages Northwest Minzu University Gansu Lanzhou730000 China

ISBN: (纸本)9789819952021

Folk songs take language as the carrier, and the singing tune and cultural connotation of singing are the important research contents of many music scholars, ethnologists, and linguists. In the process of folk song singing, the vocalization of the singing voice has an important influence on the singing voice and artistic performance. The state of the singing pronunciation can be carefully observed through the acoustic analysis of the voice. This paper will introduce the specific application of voice acoustic analysis method in the research of folk song singing. Combined with the singing characteristics of folk songs, the vocal type of singing voice can be obtained through the parameters of voice acoustic analysis. The significance and the feasibility of the application of voice acoustic analysis in folk song research will be further explored. The research parameters and conclusions are helpful to speech recognition, speech synthesis, and artificial intelligence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speech synthesis

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Entity Alignment Through Joint Utilization of Multiple Pretrained Models for Attribution Relationship 11

Entity Alignment Through Joint Utilization of Multiple Pretr...

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11th IEEE International Conference on Cyber Security and Cloud computing, CSCloud 2024

作者： Yujia, Liu Tao, Xu Zhehan, Su Xinsong, Li Kaipeng, Xue Yuxin, Liu Northwest Minzu University Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Lanzhou China Northwest Minzu University Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Lanzhou China College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350376982

Most existing models for entity alignment tasks are based on a single pre-training mode. Through multiple experiments, it has been verified that a single pre-training model is no longer sufficient to meet the needs of current entity alignment tasks. Based on considerations of multiple pre-training models, this paper proposes an entity alignment method that utilizes multiple pre-training models. The purpose of this method is to address the limited accuracy in entity alignment tasks, with a particular focus on separately pre-training the embedding of entity attribute information and link relationship information for matching. To achieve the goal of improving entity alignment accuracy, this paper adopts joint alignment using pre-training with BERT and GloVe word embeddings. Experimental evaluations are conducted on the DBP15k dataset, and the results demonstrate that leveraging multiple pre-training models for attribute and relationship joint alignment can enhance model performance. The evaluation metrics of the model on three different language datasets, Hits@1 and MRR, are as follows: 0.899, 0.930, 0.964, and 0.932, 0.951, 0.975, respectively. These metrics indicate that the model performs well in aligning entities and outperforms current mainstream entity alignment methods. © 2024 IEEE.

关键词： Embeddings

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Optimizing Antenna Design through AI Integration: A Concise Review

Optimizing Antenna Design through AI Integration: A Concise ...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Xiaotian Liu Kwok L. Chung Wenbiao Li Ruihong Li Shuwei Huang Yingsong Li School of Computer Science and Engineering Huizhou University Huizhou China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The integration of artificial intelligence with antenna design and optimization represents a promising research frontier. This paper provides a concise overview of current research, emphasizing the computational cost challenges associated with implementing machine learning in antenna design and optimization. It explores the advantages and limitations of utilizing the Kriging model and the Surrogate-assisted Differential Evolution Algorithm (SADEA) series to tackle these cost constraints.

关键词： Microwave antennas Technological innovation Analytical models Reviews Computational modeling Machine learning Transforms

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Broadband Circularly Polarized Hybrid Metasurface Antenna for Wearable Devices

Broadband Circularly Polarized Hybrid Metasurface Antenna fo...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Zhiyi Liu Yufa Sun Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University HeFei China

A 38×38×4mm3 wideband circularly polarized (CP) antenna with Hybrid Metasurface (MTS) for wearable devices is proposed. The proposed antenna achieves CP radiation by cutting corners on a square microstrip patch. A hybrid MTS is placed above a square patch with cut corners, achieving broadband radiation performance. The working mechanism of hybrid MTS is analyzed by characteristic mode analysis (CMA). The simulated -10dB bandwidth is 40.1% (4.61-6.82 GHz) and the 3dB axial ratio bandwidth is 25.2% (5.16-6.55 GHz) with a peak gain of 8.1 dBi. In addition, the antenna has a front-to-back ratio of approximately 20 dB, which makes it suitable for wearable applications.

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Acoustic Parameters Analysis of Special Singing Method of Folk Songs for Singing Speech Synthesis 3rd

Acoustic Parameters Analysis of Special Singing Method of Fo...

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3rd International Conference on Advanced intelligent Technologies, ICAIT 2022

作者： Chen, Shuang Lyu, Shiliang Qi, Hanyun Gansu Provincial Key Laboratory of Intelligent Processing of National Languages Northwest Minzu University Gansu Lanzhou730000 China Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China

ISBN: (纸本)9789819952021

Singing speech synthesis is a method to convert the information of lyrics and music score into singing speech. It usually uses statistical parametric synthesis, hidden Markov model-based method, and deep learning method. The naturalness of the synthesized singing voice has been significantly improved, but the naturalness of the special singing method for folk songs is low. Special singing methods are important methods for expressing the artistic characteristics of various folk songs, and it is also an important content in the study of folk song’s ontology. At present, in the field of artificial intelligence, singing speech recognition and synthesis has gradually become a hot research field. In the research of folk song speech recognition and synthesis, the research of special singing method is the key and difficult point. Through the method of phonetic and acoustic analysis, we can quantify and refine the study of special singing methods in folk songs. Taking the folk song "The Precious Horse on the Prairie", which is from the eastern of Yugur nationality in China as an example. Based on the speech signal acquisition, this paper performs an acoustic analysis by using Praat software to extract the fundamental frequency, intensity, jitter, shimmer, the first formant, and the third formant from the segments of drag cavity in this song. What’s more it also summarizes the acoustic characteristics of the drag cavity, in order to provide some method and data reference for the study of special singing methods of folk songs and singing speech synthesis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Music

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Robust DOA Estimation Against Outliers Based on Bias-compensated Adaptive Filtering

Robust DOA Estimation Against Outliers Based on Bias-compens...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Wudang Xiao Canping Yu Yingsong Li Liping Li Dingcheng Zou Nikola Zlatanov Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The direction-of-arrival (DOA) estimation approach utilizing the adaptive nulling array framework has gained widespread attention due to its low complexity. However, in impulsive noise environments, traditional least mean squares (LMS) algorithm may experience a sharp decline in performance or even fail to work properly. To address this challenge, we propose a DOA estimation approach designed for impulsive noise situations based on a robust nonlinear function. In addition, the algorithm can mitigate bias caused by auxiliary array noise and uses the variable step-size strategy to enhance its performance. Furthermore, the mean stability of the proposed approach is also assessed. Simulation results demonstrate its superior performance in impulsive noise environments.

关键词： Resistance Direction-of-arrival estimation Simulation Noise Estimation Adaptive arrays Information filters

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